GoodLads vs YAGNI: Features, Pricing & Which Is Better (2026)

A side-by-side comparison of GoodLads and YAGNI — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.

GoodLads

Paid

AI growth manager for Google Ads that turns account performance into testable hypotheses and ships each one only on your approval.

Hypothesis Feed: Daily analysis of search terms, keyword quality, geography, and audiences produces a ranked list of ideas, each naming the campaign and the spend at risk.
One-Click Shipping with Approval Gate: Any proposed change is applied in a single click but never without explicit owner approval, and live ads are not edited directly.
Kanban Verdict Board: Hypotheses move through Proposed, Scheduled, Live, and Completed so every test ends with a measured verdict rather than being forgotten.
Account Treemap Overview: Campaign spend, conversions, and ROAS roll into one visual overview sized by spend and coloured against the account average.
Least-Risky Lever Selection: Recommendations favour reversible mechanisms such as 50/50 RSA experiments, stepped target CPA changes, and new paused assets.
Predicted vs Measured Reporting: Each completed experiment compares the predicted lift against the actual result, with budget shifting to the winner.
Claude Code and Codex Integration: The same workflows can be driven from Claude Code or Codex for teams that work from a coding agent.

Performance Review: Get a single overview of how every campaign is doing on spend, conversions, and ROAS without building reports by hand.
Wasted Spend Discovery: Surface negative keyword opportunities, poor keyword-ad combinations, and geography issues that are draining budget.
Budget-Capped Campaigns: Identify campaigns limited by budget and lower target CPA in reversible steps to buy cheaper conversions at the same spend.
Ad Copy Testing: Run benefit-led versus price-led headline experiments as 50/50 splits instead of editing live ads.
Seasonal Campaign Prep: Stage seasonal copy and sitelink assets in advance, ready for one-click approval when demand spikes.
Agency Account Management: Manage optimisation hypotheses across multiple client accounts from one board with a shared approval workflow.

Freemium

Managed AI agent Teams with responsibilities, a number, and commitments — earn autonomy rule by rule, with receipts and playbooks.

Managed Agent Teams: Each Team owns a real part of the business with written Responsibilities, one Number it is measured on, and time-bound Commitments.
Playbook Learning: Human edits to drafts are captured as Playbook rules, so the Team's method improves and the next draft needs fewer corrections.
Training → Supervised → Autonomous Ladder: Agents earn authority rule by rule based on their track record, with per-Playbook rules bounded and logged.
Decision Queue with Receipts: Consequential calls are staged as Decisions with confidence scores; routine work runs on its own and leaves a Receipt pulled from Stripe, Gmail, Calendar, and other sources.
The Front Shared View: A single dashboard shows where the business stands right now, so humans and Teams operate from the same live picture.
Plays for Multi-Step Work: For bigger swings, a Team proposes a Play with goal, steps, budget, and deadline that runs over days once approved, still leaving Receipts at every step.
Stack-Native Integrations: Connects to Slack, Gmail, HubSpot, Stripe, GitHub, Notion, Linear, and Calendar and ships approved work back into those systems.
YAGNI Code for Engineering Teams: When work is code, Teams open pull requests with tests green and await review just like a human contributor.

Sales Pipeline Ownership: A Sales Team sources leads, books ICP-qualifying meetings, and keeps the CRM current under a monthly qualified-meetings Number.
Support Operations: A Support Team triages tickets, drafts replies, and stages refund Decisions for reversible-vs-consequential judgment calls.
Customer Success and Renewals: A CS Team drafts renewal replies, tracks at-risk accounts, and files pipeline updates for leadership.
Revenue Ops and Reporting: A Revenue Team files structured updates on pipeline movement and payments, tying results back to Stripe receipts.
Engineering Toil: A Code Team opens PRs for well-scoped fixes, with tests green and human review before merge.
Operations Backlog: An Ops Team handles routine, reversible work (scheduling, follow-ups, data hygiene) autonomously once its Playbook is trusted.